Hamy Mean Operator Research Articles

Pythagorean uncertain linguistic variable Hamy mean operator and its application to multi-attribute group decision making

Pythagorean fuzzy set &#x0028 PFS &#x0029 can provide more flexibility than intuitionistic fuzzy set &#x0028 IFS &#x0029 for handling uncertain information, and PFS has been increasingly used in multi-attribute decision making problems. This paper proposes a new multi-attribute group decision making method based on Pythagorean uncertain linguistic variable Hamy mean &#x0028 PULVHM &#x0029 operator and VIKOR method. Firstly, we define operation rules and a new aggregation operator of Pythagorean uncertain linguistic variable &#x0028 PULV &#x0029 and explore some properties of the operator. Secondly, taking the decision makers &#x02BC hesitation degree into account, a new score function is defined, and we further develop a new group decision making approach integrated with VIKOR method. Finally, an investment example is demonstrated to elaborate the validity of the proposed method. Sensibility analysis and comprehensive comparisons with another two methods are performed to show the stability and advantage of our method.

Methods for Multiple-Attribute Group Decision Making with q-Rung Interval-Valued Orthopair Fuzzy Information and Their Applications to the Selection of Green Suppliers

In the practical world, there commonly exist different types of multiple-attribute group decision making (MAGDM) problems with uncertain information. Symmetry among some attributes’ information that is already known and unknown, and symmetry between the pure attribute sets and fuzzy attribute membership sets, can be an effective way to solve this type of MAGDM problem. In this paper, we investigate four forms of information aggregation operators, including the Hamy mean (HM) operator, weighted HM (WHM) operator, dual HM (DHM) operator, and the dual-weighted HM (WDHM) operator with the q-rung interval-valued orthopair fuzzy numbers (q-RIVOFNs). Then, some extended aggregation operators, such as the q-rung interval-valued orthopair fuzzy Hamy mean (q-RIVOFHM) operator; q-rung interval-valued orthopairfuzzy weighted Hamy mean (q-RIVOFWHM) operator; q-rung interval-valued orthopair fuzzy dual Hamy mean (q-RIVOFDHM) operator; and q-rung interval-valued orthopair fuzzy weighted dual Hamy mean (q-RIVOFWDHM) operator are presented, and some of their precious properties are studied in detail. Finally, a real example for green supplier selection in green supply chain management is provided, to demonstrate the proposed approach and to verify its rationality and scientific nature.

Linguistic Intuitionistic Fuzzy Hamy Mean Operators and Their Application to Multiple-Attribute Group Decision Making

The Hamy Mean (HM) operator is a useful aggregation tool characterized by considering the correlation between multiple integral parameters. The linguistic intuitionistic fuzzy numbers (LIFNs) use linguistic variables (LVs) to represent membership degree (MD) and non-membership degree (NMD), which can flexibly and accurately represent the ambiguity in the actual decision problem. Based on the operational laws of LIFNs, we extend the HM operator to the LIFNs, and put forward the HM operator for LIFNs (LIFHM) and the weighted HM operator for LIFNs (WLIFHM). In addition, we discuss the properties of LIFHM and WLIFHM operators and some special cases, then we propose a multiple-attribute group decision making (MAGDM) method based on the WLIFHM operator. Lastly, a practical example is given to illustrate the effectiveness of the proposed operators by comparing it with different methods.

A Novel Multi-Attribute Group Decision-Making Method and Its Application in Solving the Downward Referral Problem in the Hierarchical Medical Treatment System in China

China has made many achievements in health care industry since the establishment of hierarchical medical treatment system, but also exposed a lot of problems, such as the problem of “easy to upward-referral, hard to downward-referral”, which occurred in the implementation process of two-way referral. Recently, the appearance of “hard to downward-referral” have gotten more and more attention from scholars. The key step in solving the problem of “downward-referral” is to classify hospitals at different levels evaluated by the professional doctors. This paper proposes a framework to solve the problem where evaluation values of the hospital are given as q-rung orthopair fuzzy uncertain linguistic variables (q-ROFULVs). Considering Schweizer-Sklar t-conorm and t-norm (SSTT) can make the information aggregation process more flexible, and Hamy Mean operator can consider the correlation between hospital indicators. In order to take full advantage of these two kinds of operators, firstly, we extend SSTT to q-ROFULVs and define Schweizer-Sklar operational rules of q-ROFULVs. Secondly, we combine the Hamy Mean operator with Schweizer-Sklar t-conorm and t-norm, and propose the q-rung orthopair fuzzy uncertain linguistic Schweizer-Sklar Hamy mean aggregation operators and q-Rung orthopair fuzzy uncertain linguistic Schweizer-Sklar weighted Hamy Mean respectively. Afterwards, q-rung orthopair fuzzy uncertain linguistic Schweizer-Sklar dual Hamy mean operator and its weighted form are also developed. Then, we have studied in detail some of the ideal properties of these operators. Furthermore, a novel multi-attribute group decision-making approach based on proposed operators is introduced. Finally, we apply the new approach to solve the problem about the downward conversion in the hierarchical medical treatment system. This is of great significance for integrating the medical resources of the whole society and improving the service efficiency of the medical service system.

Multi-Attribute Group Decision Making Based on Intuitionistic Uncertain Linguistic Hamy Mean Operators With Linguistic Scale Functions and Its Application to Health-Care Waste Treatment Technology Selection

How to select an appropriate and effective health-care waste treatment technology (HCW-TT) is an especially important task in the management of health-care waste (HCW), which can be regarded as a typical multi-attribute group decision making (MAGDM) problem. In the selection of HCW-TT, the expression of evaluation information given by decision makers (DMs) under uncertain decision-making environment and the reflection of interrelationships among multi-attributes are two critical issues. In response, a new MAGDM technique is presented for the selection of HCW-TT based on the intuitionistic uncertain linguistic Hamy mean with linguistic scale functions (LSFs). First, considering the lack of closeness and flexibility of existing operations, some new operations of intuitionistic uncertain linguistic variables (IULVs) are redefined by combining with LSFs. New expected value and accuracy function are also presented to compare IULVs. Then, based on the new operations of IULVs, the intuitionistic uncertain linguistic Hamy mean and its weighted version (IULWHAM) are proposed to aggregate IULVs. The proposed operators can simultaneously model the interrelationship among multi-inputs and handle the semantic translation requirements of different DMs. Meanwhile, several attractive properties and special cases of these two operators are studied and analyzed. Subsequently, a MAGDM method (IUL-MAGDM) is presented based on the proposed IULWHAM. Finally, a numerical example is given to demonstrate the proposed IUL-MAGDM method, and results indicate that the proposed IUL-MAGDM can effectively and flexibly handle the selection of HCW management by comparing with other IUL-MAGDM methods.

Intuitionistic Fuzzy Interaction Hamy Mean Operators and Their Application to Multi-attribute Group Decision Making

The Hamy mean (HM) operator, as a useful aggregation tool, is capable of capturing the correlations among multiple integrated parameters. In this paper, we expand HM operator to integrate intuitionistic fuzzy numbers (IFNs) on the basis of interaction operational rules of IFNs, and develop intuitionistic fuzzy interaction HM (IFIHM) operator and intuitionistic fuzzy weighted interaction HM (IFWIHM) operator. Moreover, we discuss some properties and particular cases of the IFIHM operator and the IFWIHM operator in detail. In addition, in real applications, there are many decision making problems with the interactions among multiple attributes, so we present a multi-attribute group decision making (MAGDM) method based on the IFWIHM operator. At the same time, the applicability and validity of the proposed MAGDM method is illustrated clearly by using an example concerning the evaluation of scenic spots. Finally, comparing with the existing approaches, the merits of the proposed MAGDM method are demonstrated, i.e., it can deal with MAGDM problems by considering the interactions among multiple attributes based on the IFWIHM operator which is a generalization of most existing operators, and it can also overcome the weaknesses existed in the traditional operational laws of IFNs.

Some Interval-Valued Intuitionistic Fuzzy Dombi Hamy Mean Operators and Their Application for Evaluating the Elderly Tourism Service Quality in Tourism Destination

In this paper, we expand the Hamy mean (HM) operator and Dombi operations with interval-valued intuitionistic fuzzy numbers (IVIFNs) to propose the interval-valued intuitionistic fuzzy Dombi Hamy mean (IVIFDHM) operator, interval-valued intuitionistic fuzzy weighted Dombi Hamy mean (IVIFWDHM) operator, interval-valued intuitionistic fuzzy dual Dombi Hamy mean (IVIFDDHM) operator, and interval-valued intuitionistic fuzzy weighted dual Dombi Hamy mean (IVIFWDDHM) operator. Then the MADM models are designed with IVIFWDHM and IVIFWDDHM operators. Finally, we gave an example for evaluating the elderly tourism service quality in tourism destination to show the proposed models.

Models for Multiple Attribute Decision Making with Some 2-Tuple Linguistic Pythagorean Fuzzy Hamy Mean Operators

The Hamy mean (HM) operator, as a useful aggregation tool, can capture the correlation between multiple integration parameters, and the 2-tuple linguistic Pythagorean fuzzy numbers (2TLPFNs) are a special kind of Pythagorean fuzzy numbers (PFNs), which can easily describe the fuzziness in actual decision making by 2-tuple linguistic terms (2TLTs). In this paper, to consider both Hamy mean (HM) operator and 2TLPFNs, we combine the HM operator, weighted HM (WHM) operator, dual HM (DHM) operator, and dual WHM (DWHM) operator with 2TLPFNs to propose the 2-tuple linguistic Pythagorean fuzzy HM (2TLPFHM) operator, 2-tuple linguistic Pythagorean fuzzy WHM (2TLPFWHM) operator, 2-tuple linguistic Pythagorean fuzzy DHM (2TLPFDHM) operator and 2-tuple linguistic Pythagorean fuzzy DWHM (2TLPFDWHM) operator. Then some multiple attribute decision making (MADM) procedures are developed based on these operators. At last, an applicable example for green supplier selection is given.

Methods for Multiple Attribute Decision Making with Interval-Valued Pythagorean Fuzzy Information

Interval-valued Pythagorean fuzzy numbers (IVPFNs) can easily describe the incomplete and indeterminate information by degrees of membership and non-membership, and the Hamy mean (HM) operator and dual HM (DHM) operators are a good tool for dealing with multiple attribute decision making (MADM) problems because it can capture the interrelationship among the multi-input arguments. Motivated by the studies regarding the HM operator and dual HM operator, we expand the HM operator and dual HM (DMM) operator to process the interval-valued Pythagorean fuzzy numbers (IVPFNs) and then to solve the MADM problems. Firstly, we propose some HM and DHM operators with IVPFNs. Moreover, we present some new methods to solve MADM problems with the IVPFNs. Finally, an applicable example is given.

Pythagorean Fuzzy Hamy Mean Operators in Multiple Attribute Group Decision Making and Their Application to Supplier Selection

In this paper, we extend the Hamy mean (HM) operator and dual Hamy mean (DHM) operator with Pythagorean fuzzy numbers (PFNs) to propose Pythagorean fuzzy Hamy mean (PFHM) operator, weighted Pythagorean fuzzy Hamy mean (WPFHM) operator, Pythagorean fuzzy dual Hamy mean (PFDHM) operator, weighted Pythagorean fuzzy dual Hamy mean (WPFDHM) operator. Then the multiple attribute group decision making (MAGDM) methods are proposed with these operators. In the end, we utilize an applicable example for supplier selection to prove the proposed methods.

Some Linguistic Neutrosophic Cubic Mean Operators and Entropy with Applications in a Corporation to Choose an Area Supervisor

In this paper, we combined entropy with linguisti neutrosophic cubic numbers and used it in daily life problems related to a corporation that is going to choose an area supervisor, which is the main target of our proposed model. For this, we first develop the theory of linguistic neutrosophic cubic numbers, which explains the indeterminate and incomplete information by truth, indeterminacy and falsity linguistic variables (LVs) for the past, present, as well as for the future time very effectively. After giving the definitions, we initiate some basic operations and properties of linguistic neutrosophic cubic numbers. We also define the linguistic neutrosophic cubic Hamy mean operator and weighted linguistic neutrosophic cubic Hamy mean (WLNCHM) operator with some properties, which can handle multi-input agents with respect to the different time frame. Finally, as an application, we give a numerical example in order to test the applicability of our proposed model.

The Location Selection for Roundabout Construction Using Rough BWM-Rough WASPAS Approach Based on a New Rough Hamy Aggregator

An adequately functionally located traffic infrastructure is an important factor in the mobility of people because it affects the quality of traffic, safety and efficiency of carrying out transportation activities. Locating a roundabout on an urban network is an imperative for road engineering to address traffic problems such as reduction of traffic congestion, enhancement of security and sustainability, etc. Therefore, this paper evaluates potential locations for roundabout construction using Rough BWM (Best Worst Method) and Rough WASPAS (Weighted Aggregated Sum Product Assessment) models. Determination of relative criterion weights on the basis of which the potential locations were evaluated was carried out using the Rough BWM method. In this paper, in order to enable the most precise consensus for group decision-making, a Rough Hamy aggregator has been developed. The main advantage of the Hamy mean (HM) operator is that it can capture the interrelationships among multi-input arguments and can provide DMs more options. Until now, there is no research based on HM operator for aggregating imprecise and uncertain information. The obtained indicators are described through eight alternatives. The results show that the fifth and sixth alternatives are the locations that should have a priority in the construction of roundabouts from the perspective of sustainable development, which is confirmed throughout changes of parameter k and with comparing to other methods in the sensitivity analysis.

Some linguistic neutrosophic Hamy mean operators and their application to multiattribute group decision making

Linguistic neutrosophic numbers (LNNs) can easily describe the incomplete and indeterminate information by the truth, indeterminacy, and falsity linguistic variables (LVs), and the Hamy mean (HM) operator is a good tool to deal with multiple attribute group decision making (MAGDM) problems because it can capture the interrelationship among the multi-input arguments.

Research on Construction Engineering Project Risk Assessment with Some 2-Tuple Linguistic Neutrosophic Hamy Mean Operators

Research on Construction Engineering Project Risk Assessment with Some 2-Tuple Linguistic Neutrosophic Hamy Mean Operators

Research on Construction Engineering Project Risk Assessment with Some 2-Tuple Linguistic Neutrosophic Hamy Mean Operators

In this paper, we expand the Hamy mean (HM) operator, weighted Hamy mean (WHM), dual Hamy mean (DHM) operator, and weighted dual Hamy mean (WDHM) operator with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose a 2-tuple linguistic neutrosophic Hamy mean (2TLNHM) operator, 2-tuple linguistic neutrosophic weighted Hamy mean (2TLNWHM) operator, 2-tuple linguistic neutrosophic dual Hamy mean (2TLNDHM) operator, and 2-tuple linguistic neutrosophic weighted dual Hamy mean (2TLNWDHM) operator. Then, the multiple attribute decision-making (MADM) methods are proposed with these operators. Finally, we utilize an applicable example in risk assessment for construction engineering projects to prove the proposed methods.

Some linguistic neutrosophic Hamy mean operators and their application to multi-attribute group decision making.

Linguistic neutrosophic numbers (LNNs) can easily describe the incomplete and indeterminate information by the truth, indeterminacy, and falsity linguistic variables (LVs), and the Hamy mean (HM) operator is a good tool to deal with multiple attribute group decision making (MAGDM) problems because it can capture the interrelationship among the multi-input arguments. Motivated by these ideas, we develop linguistic neutrosophic HM (LNHM) operator and weighted linguistic neutrosophic HM (WLNHM) operator. Some desirable properties and special cases of two operators are discussed in detail. Furthermore, considering the situation in which the decision makers (DMs) can’t give the suitable weight of each attribute directly from various reasons, we propose the concept of entropy for linguistic neutrosophic set (LNS) to obtain the attribute weight vector objectively, and then the method for MAGDM problems with LNNs is proposed, and some examples are used to illustrate the effectiveness and superiority of the proposed method by comparing with the existing methods.

Interval type-2 fuzzy Hamy mean operators and their application in multiple criteria decision making

Interval type-2 fuzzy set (IT2FS) is a widely used tool for handling vagueness and imprecision in real-world problems. To aggregate the interval type-2 fuzzy information, some useful aggregation operators have been developed such as IT2-OWA, IT2-FWA, and IT2-Frank operators. In particular, some aggregation operators which can capture and reflect and interrelationships among aggregated arguments are intensively studied, among which Hamy mean (HM) is an important alternative. However, the traditional HM does not focus on fusion of fuzzy information and the weighted vector of the aggregated arguments. In this study, we extend the HM to accommodate interval type-2 fuzzy environment and show its application to multiple criteria decision making (MADM). In order to simplify the computing, we first define the concept of symmetric triangular interval type-2 fuzzy set and propose some operational laws. Motivated by the idea of HM operator, we develop the symmetric triangular interval type-2 fuzzy Hamy mean (STIT2FHM) operator for aggregating the interval type-2 fuzzy information. Some useful properties such as monotonicity, boundedness, and idempotency are studied in detail. Furthermore, we discuss some special cases with respect to different parameter values of the STIT2FHM operator. For the situations where the input arguments have different importance, we further develop the weighted symmetric triangular interval type-2 fuzzy Hamy mean (WSTIT2FHM) operator to aggregate interval type-2 fuzzy information. Based on it, an approach to MCDM problems with symmetric triangular interval type-2 fuzzy information is developed. Finally, a real-world example concerning tourism recommender system is provided to illustrate the practicality and effectiveness of the proposed method.